AIs are inching ever-closer to a critical threshold. Beyond this threshold lie great risks—but crossing it is not inevitable.

Intelligence explosion, singularity, fast takeoff… these are a few of the terms given to the surpassing of human intelligence by machine intelligence, likely to be one of the most consequential – and unpredictable – events in our history.

For many decades, scientists have predicted that artificial intelligence will eventually enter a phase of recursive self-improvement, giving rise to systems beyond human comprehension, and a period of extremely rapid technological growth. The product of an intelligence explosion would be not just Artificial General Intelligence (AGI) – a system about as capable as a human across a wide range of domains – but a superintelligence, a system that far surpasses our cognitive abilities.

Speculation is now growing within the tech industry that an intelligence explosion may be just around the corner. Sam Altman, CEO of OpenAI, kicked off the new year with a blog post entitled Reflections, in which he claimed: “We are now confident we know how to build AGI as we have traditionally understood it… We are beginning to turn our aim beyond that, to superintelligence in the true sense of the word”. A researcher at that same company referred to controlling superintelligence as a “short term research agenda”. Another’s antidote to online hype surrounding recent AI breakthroughs was far from an assurance that the singularity is many years or decades away: “We have not yet achieved superintelligence”.

Introducing our new AI awareness companion; notes from the AI Action Summit and IASEAI; a new short film on AI replacing human labour; and more!

How employees and C-suite executives view select areas of AI adoption at their company

AI adoption in the workplace is deepening divisions and sparking new power struggles between leaders and workers, with half of executives saying that AI is “tearing their company apart,” according to new research from Writer, the enterprise AI startup.

The big picture: Executives are pushing AI as an inevitable revolution, but workers aren’t buying it.

Driving the news: Nearly all (94%) C-suite execs surveyed say they’re not satisfied with their current AI solution.

The bottom line: C-suite execs tout AI as a competitive necessity and urge workers to get on board — but broken tools and employees’ job fears continue to make the road to AI adoption rocky.

The big AI companies have already siphoned up the bulk of what’s to be had in the way of data from the internet. This publicly available data is easy to acquire but a mixed bag in terms of quality. If data is the fuel of AI, the holy grail for a big tech company is tapping into a new reserve of high quality data, especially if you have exclusive access. That reserve of data is collected – and guarded – by government agencies.

Government databases capture real decisions and their consequences and contain verified records of actual human behavior across entire populations over time, writes Middlebury public policy expert Allison Stanger.

“Unlike the disordered information available online, government records follow standardized protocols, undergo regular audits and must meet legal requirements for accuracy,” she writes. “For companies seeking to build next-generation AI systems, access to this data would create an almost insurmountable advantage.”

While the comparison with DeepSeek might make sense from marketing and geopolitical standpoints, it is important to remember that these are two different AI applications with different strategies and functionalities:

• DeepSeek-R1 is an open-source, general-purpose AI model designed to rival OpenAI’s o1;
• Manus AI is a general AI agent currently in closed beta testing. It requires an invitation to access and is not entirely open-source;
• Manus AI hasn’t triggered a significant stock drop like the one Nvidia saw after DeepSeek.

I believe that the federal government should possess a robust capacity to evaluate the capabilities of frontier AI systems to cause catastrophic harms in the hands of determined malicious actors. Given that this is what the US AI Safety Institute does, I believe it should be preserved. Indeed, I believe its funding should be increased. If it is not preserved, the government must rapidly find other ways to maintain this capability. That seems like an awful lot of trouble to go through to replicate an existing governmental function, and I do not see the point of doing so. In the longer term, I believe AISI can play a critical nonregulatory role in the diffusion of advanced AI—not just in catastrophic risk assessment but in capability evaluations more broadly.

Why AISI is Worth Preserving

Shortly after the 2024 election, I wrote a piece titled “AI Safety Under Republican Leadership.” I argued that the Biden-era definition of “AI safety” was hopelessly broad, incorporating everything from algorithmic bias and misinformation to catastrophic and even existential risks. In addition to making it impossible to execute an effective policy agenda, this capacious conception of AI safety also made the topic deeply polarizing. When farcical examples of progressive racial neuroses manifested themselves in Google Gemini’s black Founding Fathers and Asian Nazis, critics could—correctly—say that such things stemmed directly from “AI safety policy.”

AI Policy, Compliance & Regulation

In 2024, with the enactment of the EU AI Act, the release of numerous AI governance frameworks, and the launch of new professional certifications, we saw a surge in AI governance professionals entering the workforce, particularly in the U.S. and Europe.

But what exactly is an AI governance professional, and what kinds of jobs could fit this definition?

Many people assume that being an AI governance professional requires a legal degree. This is a misconception. AI governance, from a professional perspective, is an umbrella term encompassing various fields, skills, and areas of expertise.

How employees and C-suite executives view select areas of AI adoption at their company

AI adoption in the workplace is deepening divisions and sparking new power struggles between leaders and workers, with half of executives saying that AI is “tearing their company apart,” according to new research from Writer, the enterprise AI startup.

The big picture: Executives are pushing AI as an inevitable revolution, but workers aren’t buying it.

Driving the news: Nearly all (94%) C-suite execs surveyed say they’re not satisfied with their current AI solution.

The bottom line: C-suite execs tout AI as a competitive necessity and urge workers to get on board — but broken tools and employees’ job fears continue to make the road to AI adoption rocky.

After safety took a back seat at the Paris AI Action Summit, Western governments have made a clear pivot: Winning the AI race is more important than regulating it. Now, a mind-boggling global spending spree is on. The U.S. is investing $500 billion in the Stargate project. The EU launched the €200 billion (about $215 billion) InvestAI initiative, France has announced €109 billion (about $117 billion) and the U.K. has announced at least £20 billion (about $26 billion) in data center investments since October.

British Prime Minister Keir Starmer summed up the new approach in January: “In a world of fierce competition, we cannot stand by. We must move fast and take action to win the global race.”

As nations plan massive investments into data centers, it’s also becoming clear money is not the only factor to drive AI development. Other factors like workforce training and access to energy and semiconductors are subject to policies that could pull countries in a different direction.

Last Thursday, the Chinese AI startup Monica released an agent called Manus. In a demo video, co-founder Yichao “Peak” Ji described the system as “the first general AI agent,” capable of doing a wide range of tasks using a computer like a human would. While numerous startups, as well as OpenAI and Anthropic, have released general computer-using agents, the company itself claims superior performance to those products. Many reports from social media seem to agree, though there are notable exceptions.

Manus is not available to the general public as of this writing. Monica has given access to a select group of users—seemingly focused on high-profile influencers. I was not offered an access code, but I was able to use the system for a couple of prompts. My tentative conclusion from that experience—as well as the uses I have seen from others—is that Manus is the best general-purpose computer use agent I have ever tried, though it still suffers from glitchiness, unpredictability, and other problems.

Some have speculated that Manus represents another “DeepSeek moment,” where a Chinese startup is surprisingly competitive with top-tier American offerings. I suspect this analogy confuses more than it clarifies. DeepSeek is a genuine frontier AI lab. They are on a quest to build AGI in the near term, have a deep philosophical conviction about the power of deep learning, and are staffed with a team of what Anthropic Co-Founder Jack Clark has called “unfathomable geniuses.”

Now, our POLITICO U.K. colleague Tom Bristow has gotten a peek at a British government document with new details of London’s ideas for a trade pact with the U.S. It offers a look at how a new global AI consensus could take shape — with much less worry about safety, and much more concern about security and tech dominance.

What’s in the document? The paper outlines the pitch the U.K. plans to make to the U.S., and it echoes rhetoric used by Vance and Trump that countries must choose whether to side with or against the U.S. on tech policy. It talks about combining British and American “strengths” so that Western democracies can win the tech race — language that British Technology Secretary Peter Kyle has increasingly started to use in recent weeks — and signals ever-closer alignment with the U.S. on tech.

The document outlines Britain’s ambitions for an “economic partnership” on technology. It pitches the case by pointing out that the U.S. and U.K. are the only two allies in the world with trillion-dollar tech industries, and emphasizes the importance of Western democracies beating rivals to cutting-edge breakthroughs.

By January of 2020, 80.6 percent of prime-age workers had jobs. That measure cratered during Covid, but bounced back rapidly to 80.9 percent by June of 2023. The Obama-era labor market wasn’t sluggish because of ATMs — it was sluggish because policymakers were inflation-averse and settled for a slow recovery. In 2020, a different set of policymakers made different choices and got different results.

That was a lot of throat-clearing because I want to establish my bona fides before I say this: I think it’s time to think seriously about AI-induced job displacement. …

Today’s large language models, with their vastly increased complexity and capability, create an even more compelling illusion of understanding. When interacting with these systems, users often project meaning, intent, and comprehension onto the AI’s responses, even when the system is merely producing statistically likely sequences of words based on its training data. This illusion of understanding has profound implications:

1. Over-reliance on AI Systems: Users may place undue trust in AI-generated content, assuming a level of comprehension and reliability that doesn’t actually exist.

2. Anthropomorphizing: The tendency to attribute human-like qualities to these systems can lead to unrealistic expectations and potential disappointment.

Lewis’ warning is not simply that a scientific, techno elite will govern us, but that we will let them. The creeping bureaucratization and commodification of life, the slow erosion of faith, the elevation of efficiency above meaning, these are not external forces imposed upon an unwilling populace, but rather the logical result of our own acquiescence.

If Orwell’s 1984 was a warning against totalitarianism and Huxley’s Brave New World a warning against hedonistic dystopia, then That Hideous Strength is a warning against the slow, bureaucratic suffocation of the human spirit. It is a novel that deserves to be read not simply as a piece of fiction, but as a reflection of our present age, revealing both its perils and its possibilities.

Lewis does not leave us in despair, he offers us a question that leaves us thinking beyond the final pages: In the face of an all-consuming bureaucracy, where do we take our stand?

Stay curious

OpenAI’s Deep Research is built for me, and I can’t use it. It’s another amazing demo, until it breaks. But it breaks in really interesting ways.

“And then a miracle happens” is not a plan

It is an article of faith among Andreessen-style techno-optimists that “there is no material problem – whether created by nature or by technology – that cannot be solved with more technology.” Take this reasoning just one step further, and we can find in the dismantling of the administrative state a tremendous opportunity to replace human intelligence with machine intelligence. It just requires confidence that machine intelligence will improve fast enough to meet the need.

Silicon Valley luminaries apply the same logic to climate change. Sam Altman and his peers have taken to insisting that (1) the energy costs of artificial intelligence will grow exponentially and (2) imminent breakthroughs in cold fusion, aided by advances in AI, will sate this otherwise insatiable demand. Just a few months ago, in fact, Eric Schmitt stated, “we’re not going to hit the climate goals anyway because we are not organized to do it and yes the needs in this area [AI] will be a problem. But I’d rather bet on AI solving the problem than constraining it.” (h/t Gary Marcus)

This week, we interviewed Jack Clark, co-founder and head of policy at Anthropic, the company behind frontier artificial intelligence models like Claude. Before this role, Clark was OpenAI’s policy director. We talked about how people are underestimating what AI will be able to do in a few years, hardening export controls to ensure AI technology is not stolen, and the power of the belief that scaling up compute will mean better AI.

What’s one underrated big idea?

People underrate how significant and fast-moving AI progress is. We have this notion that in late 2026, or early 2027, powerful AI systems will be built that will have intellectual capabilities that match or exceed Nobel Prize winners. They’ll have the ability to navigate all of the interfaces… they will have the ability to autonomously reason over kind of complex tasks for extended periods. They’ll also have the ability to interface with the physical world by operating drones or robots. Massive, powerful things are beginning to come into view, and we’re all underrating how significant that will be.

Here are three ways of thinking about what “humans in the loop” can mean.

1. AI assists humans

Chatbots need us to prompt them or give them instructions in order to work. Agents are also assistants, but they require less supervision from humans.

• As agents’ abilities grow, keeping humans in the loop ensures “that AI systems make decisions that align with human judgment, ethics, and goals,” Fay Kallel, VP of product and design at Intuit Mailchimp, told Axios in an email.
• “By automating tedious tasks, we create space for creative and strategic work,” Kelly Moran, VP of engineering, search and AI at Slack, told Axios.
• “Humans aren’t always rowing the boat — but we’re very much steering the ship,” Paula Goldman, chief ethical and humane use officer at Salesforce, wrote last year.

The race for Generative AI Search Advanced capabilities: overview of 2025 developments and tools

Reasoning models
OpenAI’s o1 and o3 model has ushered in a new era of reasoning models implicated also as an additional layer in our research. This is clearly what makes OpenAI Deep Research so great. However now Grok 3, Claude 3.7 Sonnet and GPT-4.5 also add their own strengths into the mix, although GPT-4.5 isn’t a reasoning model, Claude 3.7 Sonnet is a hybrid reasoning model. Some users also really like Grok 3.

These new layers of search, research and reasoning models in 2025 are changing the interface of how we do Search. We knew 2025 was going to be excited on the search capabilities front, but it’s starting to really feel different to the internet that came before, it’s a noticeable departure from past consumer behaviors.

Today we continue this survey with global perspectives. When we talk about future leaders and companies that don’t yet exist, the leading robotics companies of tomorrow could be one day more lucrative than OpenAI or Anthropic. OpenAI have flirted with robotics as well, but are more likely to acquire a leading startup than build anything in-house that will be competitive.

With Apple and Meta getting robot curious, Tesla and leading robotics startups may be catalyst for a new hype boom in Humanoid general purpose robots, the holy grail of robotics.

The big AI companies have already siphoned up the bulk of what’s to be had in the way of data from the internet. This publicly available data is easy to acquire but a mixed bag in terms of quality. If data is the fuel of AI, the holy grail for a big tech company is tapping into a new reserve of high quality data, especially if you have exclusive access. That reserve of data is collected – and guarded – by government agencies.

Government databases capture real decisions and their consequences and contain verified records of actual human behavior across entire populations over time, writes Middlebury public policy expert Allison Stanger.

“Unlike the disordered information available online, government records follow standardized protocols, undergo regular audits and must meet legal requirements for accuracy,” she writes. “For companies seeking to build next-generation AI systems, access to this data would create an almost insurmountable advantage.”

The threat of a company putting its AI model on steroids with government data goes beyond unfair competition and even individual privacy concerns, writes Stanger. Such a model would give the company that wields it extraordinary power to predict and influence the behavior of populations.

This threat is more than just a thought exercise. Elon Musk is at the helm of both the Department of Government Efficiency, which has unprecedented access to U.S. government data, and the AI company xAI. The Trump administration has stated that Musk is not using the data for his company, but the temptation to do so must be quite strong.

Organizations are making big bets on generative AI.

Nearly 80% of business and IT leaders expect gen AI to drive significant transformation in their industries in the next three years.1 Global private investments in gen AI have skyrocketed, increasing from roughly US$3 billion in 2022 to US$25 billion in 2023.2 And that pace continues unabated with some US$40 billion in global enterprise investments projected in 2024 and more than US$150 billion by 2027.3 These efforts toward transformation may take on added importance as economists anticipate that labor force participation will decline in the coming years as the population ages, suggesting a need to boost productivity.4 In a world where some forecasts suggest dramatic advances like artificial general intelligence may be possible by the end of this decade,5 or a digital twin attending meetings on your behalf within five years,6 the possibilities for gen AI seem limited only by one’s imagination.

It’s a world where most don’t want to be left behind: Online references to FOMO—the “fear of missing out”—have increased more than 60% in the past five years.7 Though not specific to gen AI alone, the sentiment captures the reality that uncertainty underlies any bet, and the level of uncertainty regarding gen AI’s impact on the enterprise is significant.8 Predictions for growth and opportunity highlight one possible future, but a future where AI advances slow down and organizations encounter significant financial barriers to scaling and growing their AI capabilities is also possible.
How can organizational leaders wrap their minds around the future of AI in the enterprise and develop the best strategies to meet whatever comes?

The tech industry is throwing its weight behind science and tech work in government in response to the roller coaster of federal employee firings and rehirings and the specter of more job and budget cuts.

Why it matters: Chaos at federal agencies is taking a toll on universities and could impact the private sector — the government’s key partners in pushing forward AI and other new technologies.

Driving the news: Tech industry and advocacy groups sent a letter to Commerce Secretary Howard Lutnick today warning that agency cuts could hobble America’s global leadership in AI.